Method for history matching a simulation model using self organizing maps to generate regions in the simulation model

ABSTRACT

A method of history matching a simulation model is disclosed comprising: (a) defining regions exhibiting similar behavior in the model thereby generating the model having a plurality of regions, each of the plurality of regions exhibiting a similar behavior; (b) introducing historically known input data to the model; (c) generating output data from the model in response to the historically known input data; (d) comparing the output data from the model with a set of historically known output data; (e) adjusting the model when the output data from the model does not correspond to the set of historically known output data, the adjusting step including the step of arithmetically changing each of the regions of the model; and (f) repeating steps (b), (c), (d), and (e) until the output data from the model does correspond to the set of historically known output data.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a Utility Application of prior pending Provisional ApplicationSer. No. 60/774,589, filed Feb. 17, 2006, and entitled “Method forHistory Matching a Simulation Model using Self Organizing Maps togenerate Regions in the Simulation Model”.

BACKGROUND

This specification discloses a method, and its associated System andProgram Storage Device and Computer Program, adapted for ‘historymatching’ of numerical simulation models using a Self Organizing Map(SOM) software, the SOM being used to generate and define the ‘Regions’among the grid blocks of the numerical simulation model during thehistory matching procedure.

History matching of numerical models is an inverse problem. That is, anumerical simulation model is adjusted such that, when a set ofhistorically known input parameters are input to the model, a set ofhistorically known output parameters or data will be generated by themodel. History matching is therefore a trial and error procedure.

When ‘history matching’ a numerical simulation model, a set ofhistorically known output parameters should be generated by the model inresponse to a set of historically known input parameters. However, whenthe set of historically known output parameters are not generated by themodel in response to the set of historically known input parameters, itis necessary to multiply the value of a parameter (e.g. permeability)associated with each grid block of the numerical simulation model by acertain value. However, it is clear that the multiplier cannot be thesame number for each grid block of the model. Therefore, when thesimulation model represents a reservoir field, such as an oil or gasreservoir field, the engineer defines one or more ‘regions’ of thereservoir, wherein the same multiplier within a particular ‘region’ canbe used to improve the history match. The selection of the ‘regions’ ofthe reservoir field can be accomplished in accordance with a geologicalmodel of the reservoir. Very often, one or more ‘rectangular boxes’ areused to define the ‘regions’ of the reservoir field. However, theselection of ‘rectangular boxes’ to define the ‘regions’ of thereservoir field does not ordinarily comply with nature.

In addition, the selection of ‘regions’ in accordance with a geologicalmodel is very often based on ‘static geological information’, that is,geological information that is not directly related to hydraulicparameters associated with production from a reservoir or other changesover time (e.g. permeability is derived from a correlation with porosityafter the creation of the geological model).

SUMMARY

One aspect of the present invention involves a method of historymatching a simulation model, comprising: (a) defining regions exhibitingsimilar behavior in the model thereby generating the model having aplurality of regions, each of the plurality of regions exhibiting asimilar behavior; (b) introducing historically known input data to themodel; (c) generating output data from the model in response to thehistorically known input data; (d) comparing the output data from themodel with a set of historically known output data; (e) adjusting themodel when the output data from the model does not correspond to the setof historically known output data, the adjusting step including the stepof arithmetically changing each of the regions of the model; and (f)repeating steps (b), (c), (d), and (e) until the output data from themodel does correspond to the set of historically known output data.

Another aspect of the present invention involves a program storagedevice readable by a machine tangibly embodying a program ofinstructions executable by the machine to perform method steps forhistory matching a simulation model, the method steps comprising: (a)defining regions exhibiting similar behavior in the model therebygenerating the model having a plurality of regions, each of theplurality of regions exhibiting a similar behavior; (b) introducinghistorically known input data to the model; (c) generating output datafrom the model in response to the historically known input data; (d)comparing the output data from the model with a set of historicallyknown output data; (e) adjusting the model when the output data from themodel does not correspond to the set of historically known output data,the adjusting step including the step of arithmetically changing each ofthe regions of the model; and (f) repeating steps (b), (c), (d), and (e)until the output data from the model does correspond to the set ofhistorically known output data.

Another aspect of the present invention involves a computer programadapted to be executed by a processor, the computer program, whenexecuted by the processor, conducting a process for history matching asimulation model, the process comprising: (a) defining regionsexhibiting similar behavior in the model thereby generating the modelhaving a plurality of regions, each of the plurality of regionsexhibiting a similar behavior; (b) introducing historically known inputdata to the model; (c) generating output data from the model in responseto the historically known input data; (d) comparing the output data fromthe model with a set of historically known output data; (e) adjustingthe model when the output data from the model does not correspond to theset of historically known output data, the adjusting step including thestep of arithmetically changing each of the regions of the model; and(f) repeating steps (b), (c), (d), and (e) until the output data fromthe model does correspond to the set of historically known output data.

Another aspect of the present invention involves a system adapted forhistory matching a simulation model, comprising: first apparatus adaptedfor defining regions exhibiting similar behavior in the model therebygenerating the model having a plurality of regions, each of theplurality of regions exhibiting a similar behavior; second apparatusadapted for introducing historically known input data to the model;third apparatus adapted for generating output data from the model inresponse to the historically known input data; fourth apparatus adaptedfor comparing the output data from the model with a set of historicallyknown output data; fifth apparatus adapted for adjusting the model whenthe output data from the model does not correspond to the set ofhistorically known output data, the fifth apparatus including sixthapparatus adapted for arithmetically changing each of the regions of themodel; and seventh apparatus adapted for repeating the functionsperformed by the second, third, fourth, fifth, and sixth apparatus untilthe output data from the model does correspond to the set ofhistorically known output data.

Further scope of applicability will become apparent from the detaileddescription presented hereinafter. It should be understood, however,that the detailed description and the specific examples set forth beloware given by way of illustration only, since various changes andmodifications within the spirit and scope of the invention, as describedand claimed in this specification, will become obvious to one skilled inthe art from a reading of the following detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

A full understanding will be obtained from the detailed descriptionpresented hereinbelow, and the accompanying drawings which are given byway of illustration only and are not intended to be limitative to anyextent, and wherein:

FIG. 1 illustrates a workstation or other computer system wherein thenumerical simulation model and the Self Organizing Map (SOM) software isstored;

FIG. 2 illustrates a grid block of the numerical simulation model whichhas a ‘parameter’ associated therewith;

FIG. 3 illustrates the numerical simulation model including a pluralityof grid blocks and a method for history matching the numericalsimulation model including the method as disclosed in this specificationfor history matching a simulation model using Self Organizing Maps togenerate Regions in the simulation model;

FIG. 3A illustrates a realistic example of the numerical simulationmodel including the plurality of grid blocks;

FIG. 4 illustrates the numerical simulation model including a pluralityof grid blocks, the model including a plurality of ‘regions’ where each‘region’ of the model further includes one or more of the grid blocks ofthe numerical simulation model;

FIG. 5 illustrates how the ‘parameters’ (in addition to ‘all availableinformation’) associated with each grid block of the numericalsimulation model are introduced, as input data, to the Self OrganizingMap (SOM) software, and the SOM software responds by defining the‘regions’ of the numerical simulation model which are illustrated inFIG. 4;

FIG. 6 illustrates how ‘all available information’ associated with eachof the grid blocks of the numerical simulation model is used by the SOMsoftware to generate and define ‘regions’ of similar behavior among thegrid blocks of the numerical simulation model, and, responsive thereto,the SOM software organizes the grid blocks of the numerical simulationmodel into one or more of the defined ‘regions’ as illustrated in FIG.4; and

FIG. 7 illustrates a block diagram which describes how the SOM softwarewill define ‘regions’ of similar behavior among the grid blocks of thenumerical simulation model.

DETAILED DESCRIPTION

This specification discloses a ‘Method for history matching using SelfOrganizing Maps (SOM) to generate regions’, wherein the novel methoduses Self Organizing Maps (“SOM”) to compute ‘regions’ of similarbehavior among the grid blocks of a numerical simulation model when‘history matching’ the numerical simulation model. This leads to a muchfaster approach to a correct solution. Instead of hundreds of simulationruns, less than 20 simulation runs are generally necessary in order toachieve a good understanding of the parameter values within the gridblocks of the model. When a good understanding of such parameter valuesis achieved, a good ‘history match’ of the numerical simulation model isthe result.

A first step associated with the ‘Method for history matching using SelfOrganizing Maps (SOM) to generate regions’, as disclosed in thisspecification, uses SOM to build a set of ‘regions’ among the gridblocks of the numerical simulation model. That is, instead of groupinggrid blocks in accordance with geology, the grid blocks are grouped inaccordance with ‘regions of similar behavior based on all availableinformation’ (hereinafter called ‘regions’). The method of SelfOrganizing Maps (SOM) is used to cluster grid blocks of similarbehavior. SOMs can handle all different types of parameters, includingmodel parameters from the initialization, such as initial pressure andsaturation. This ‘new approach’ (i.e., using SOMS to generate ‘regions’)takes into account several different ‘parameters’ of each grid block ofthe model reflecting different physical and numerical processes ofhydrocarbon production, including:

-   -   geological description: such as lithofacies type    -   hydraulic flow units (HFU): such as permeabilities, porosities    -   initialization: such as water saturations (initial and        critical), initial pressure    -   discretization: such as spatial discretization (e.g. DZ), grid        block pore volumes    -   PVT regions    -   drainage    -   secondary phase movement: relative permeability endpoints.

Depending on the importance of the parameter of each grid block, itsinfluence can be controlled using a weight factor. This factor isnormalized between 0 and 1. The parameter gets the highest weight whenthe weight factor is one. A parameter has no influence on the clusteringwhen the weight factor is set to 0. The SOM generates rules which areused to identify ‘regions’ automatically. For example, a rule for onespecific ‘region’ might be:

-   -   IF DZ>10.23 AND DZ<27.48 AND    -   IF PERMX>9.03 AND PERMX<2496.5 AND    -   IF PERMY>8.53 AND PERMY<665.9 AND    -   IF PERMZ>0.89 AND PERMZ<440.8 AND    -   IF PORO>0.077 AND PORO<0.25 AND    -   IF PORV>1.38e+5 AND PORV<5.26e+5 AND    -   IF PINI>2485.5 AND PINI<2874.4 AND    -   IF SWAT>0.06 AND SWAT<0.74    -   THEN Grid-Block belongs to REGION 1

The advantage of this ‘new approach’ is its simplicity. Since the SelfOrganizing Map (SOM) is a self-learning approach, it does not need anyexpert knowledge to use this technology. The only decision which theuser has to make is how many ‘regions’ the user wants to create.

A second step associated with the ‘Method for history matching usingSelf Organizing Maps (SOM) to generate regions’, as disclosed in thisspecification, includes calculating a Root Mean Square (RMS) error basedon ‘regions’. To accelerate the match progress, it is necessary tocalculate the root mean square (RMS) error based on regions. This meansthat the direct impact of a parameter change of a region can be comparedto the quality of the match in that region. To do that, it is necessaryto split up the RMS error per well into the fractions which arecontributed by each individual region. Each region, in which a well isperforated, contributes in a different way to the well behavior. As thewell behavior is mainly driven by its production, it is also clear thatthe importance of a region in the well is depending on the product ofpermeability and thickness (kh). The higher the kh of a region in theperforated part of a well is, the higher its contribution to productionwill be. This principle is used to split up the well RMS error into anerror for each region in which the well is perforated. Summing up allwell RMS for one region can be used to determine a regional RMS value.In this way, the direct impact of a change in the region input parametercan be quantified directly.

The ‘Method for history matching using Self Organizing Maps (SOM) togenerate regions’, as disclosed in this specification, represents aclear improvement to: the ‘quality of the history match’ and the ‘numberof runs needed to achieve the history match’ of the numerical simulationmodel.

Referring to FIG. 1, a workstation, personal computer, or other computersystem 10 is illustrated adapted for storing a numerical simulationmodel 12 and a Self Organizing Map (SOM) software 14. The computersystem 10 of FIG. 1 includes a processor 10 a operatively connected to asystem bus 10 b, a memory or other program storage device 10 coperatively connected to the system bus 10 b, and a recorder or displaydevice 10 d operatively connected to the system bus 10 b. The memory orother program storage device 10 c stores the numerical simulation model12 and the Self Organizing Map (SOM) software 14 which provides an inputto and receives an output from the numerical simulation model 12. Thenumerical simulation model 12 and the Self Organizing Map (SOM) software14 which is stored in the memory 10 c of FIG. 1 can be initially storedon a CD-ROM, where that CD-ROM is also a ‘program storage device’. ThatCD-ROM can be inserted into the computer system 10, and the numericalsimulation model 12 and the Self Organizing Map (SOM) software 14 can beloaded from that CD-ROM and into the memory/program storage device 10 cof the computer system 10 of FIG. 1. The computer system 10 of FIG. 1receives ‘input data’ 16 which includes ‘historically known input data’18. The processor 10 a will execute the numerical simulation model 12and the Self Organizing Map (SOM) software 14 stored in memory 10 cwhile simultaneously using the ‘input data’ 16 including the‘historically known input data’ 18; and, responsive thereto, theprocessor 10 a will generate ‘Output Data’ 20 which is adapted to berecorded by or displayed on the Recorder or Display device 10 d inFIG. 1. The computer system 10 of FIG. 1 will attempt to ‘history match’the numerical simulation model 12 with respect to the ‘historicallyknown input data’ 18 and the ‘output data’ 20 (to be discussed later inthis specification) by using the SOM software 14 to achieve the match.The computer system 10 of FIG. 1 may be a personal computer (PC), aworkstation, a microprocessor, or a mainframe. Examples of possibleworkstations include a Silicon Graphics Indigo 2 workstation or a SunSPARC workstation or a Sun ULTRA workstation or a Sun BLADE workstation.The memory or program storage device 10 c (including the abovereferenced CD-ROM) is a computer readable medium or a program storagedevice which is readable by a machine, such as the processor 10 a. Theprocessor 10 a may be, for example, a microprocessor, microcontroller,or a mainframe or workstation processor. The memory or program storagedevice 10 c, which stores the numerical simulation model 12 and the SelfOrganizing Map (SOM) software 14, may be, for example, a hard disk, ROM,CD-ROM, DRAM, or other RAM, flash memory, magnetic storage, opticalstorage, registers, or other volatile and/or non-volatile memory.

Referring to FIGS. 2, a grid block 22 is illustrated. The grid block 22is only one grid block among a multitude of other grid blocks whichcomprise the numerical simulation model 12, each grid block includinggrid block 22 having one or more ‘parameters’ 24 associated therewith.For example, the ‘parameters’ 24 associated with the grid blocks(including grid block 22) may include permeability or transmissibilityor pore volume, as fully described and set forth in U.S. Pat. Nos.6,078,869 and 6,018,497 to Gunasekera, the disclosures of which areincorporated by reference into this specification. As noted above, the‘parameters’ could also include: geological description, such aslithofacies type, hydraulic flow units (HFU), such as permeabilities,porosities,initialization, such as water saturations (initial andcritical), initial pressure discretization, such as spatialdiscretization (e.g. DZ), grid block pore volumes, PVT regions,drainage, and secondary phase movement, such as relative permeabilityendpoints.

Referring to FIG. 3, a method for ‘history matching’ the numericalsimulation model 12 with respect to the ‘historically known input data’18 and the ‘output data’ 20 of FIG. 1 is discussed below with referenceto FIG. 3. In FIG. 3, the numerical simulation model 12 includes aplurality of the grid blocks 22, each of the plurality of grid blocks 22of FIG. 3 having one or more ‘parameters’ 24 associated therewith, suchas permeability or transmissibility or pore volume. In FIG. 3, when‘history matching’ the numerical simulation model 12, the ‘historicallyknown input data’ is introduced as ‘input data’ to the model 12 and,responsive thereto, the ‘output data’ 20 is generated. That ‘outputdata’ 20 is compared against a set of ‘historically known output data’which was previously generated (in the past) in response to the‘historically known input data’. When the ‘output data’ 20 does notsubstantially match the ‘historically known output data’, the numericalsimulation model 12 must first be ‘adjusted’ before the ‘historicallyknown input data’ 18 can again be introduced as ‘input data’ to themodel 12. In order to ‘adjust’ the model 12, refer to steps or block 26a and 26 b of FIG. 3. In step 26 a, to ‘adjust’ the model 12, certain‘regions’ must be defined in the numerical simulation model 12. When the‘regions’ are defined in the numerical simulation model 12, in step 26b, it is necessary to multiply the ‘parameters’ 24 in each grid block ofeach ‘region’ by a certain value. At this point, the model 12 has been‘adjusted’. Then, the ‘historically known input data’ 18 isreintroduced, as ‘input data’, to the model 12, and, responsive thereto,the ‘output data’ 20 is generated once again. That ‘output data’ 20 iscompared against a set of ‘historically known output data’ which waspreviously generated (in the past) in response to the ‘historicallyknown input data’. When the ‘output data’ 20 does not substantiallymatch the ‘historically known output data’, the numerical simulationmodel 12 must be ‘re-adjusted’ before the ‘historically known inputdata’ 18 can again be introduced as ‘input data’ to the model 12. Instep 26 b, in order to ‘re-adjust’ the model 12, it is necessary tomultiply the parameters 24 in each grid block of each ‘region’ by acertain value. At this point, the model 12 has been ‘re-adjusted’. Then,the ‘historically known input data’ 18 is reintroduced, as ‘input data’,to the model 12, and, responsive thereto, the ‘output data’ 20 isgenerated once again. This process repeats until the ‘output data’ 20does, in fact, substantially match the ‘historically known output data’.At this point, the numerical simulation model 12 has been ‘historymatched’.

Referring to FIG. 3A, a realistic illustration of a typical numericalsimulation model 12 of FIG. 3 is illustrated. Note the multitude of gridblocks 22 which have the ‘parameters’ 24 of FIG. 2 associated therewith.

Referring to FIG. 4, the numerical simulation model 12 of FIG. 3 isillustrated including a plurality of grid blocks 22. In FIG. 4, themodel 12 includes a plurality of ‘regions’ 30 where each ‘region’ 30 ofthe model 12 further includes one or more of the grid blocks 22, eachgrid block 22 having ‘parameters’ 24 of FIG. 2 associated therewith.Recall that, in order to ‘history match’ the numerical simulation model12, certain ‘regions’ 30 must be defined in the numerical simulationmodel 12. When the ‘regions’ 30 are defined in the numerical simulationmodel 12, in step 26 b of FIG. 3, it is necessary to multiply the‘parameters’ 24 (of FIG. 2) in each grid block 22 of the ‘region’ 30 bya certain value. At this point, the model 12 has been ‘adjusted’.

Referring to FIGS. 4 and 5, referring initially to FIG. 5, the‘parameters’ P1, P2, . . . , and P10 associated with each grid block 22(in addition to ‘all available information’ associated with each gridblock 22) of the numerical simulation model 12 are introduced, as inputdata, to the Self Organizing Map (SOM) software 14, and, responsivethereto, the SOM software 14 responds by defining the ‘regions’ 30 ofthe numerical simulation model 12 which are illustrated in FIG. 4. Inparticular, the SOM software 14 will define the ‘regions’ 30 ‘of similarbehavior’ within the numerical simulation model 12. For example, in FIG.4, the SOM software 14 of FIG. 5 will define: (1) a first ‘region 1’ 30a having a first particular type of similar behavior, (2) a second‘region 2’ 30 b having a second particular type of similar behavior, (3)a third ‘region 3’ 30 c having a third particular type of similarbehavior, (4) a fourth ‘region 4’ 30 d having a fourth particular typeof similar behavior, (5) a fifth ‘region 5’ 30 e having a fifthparticular type of similar behavior, (6) a sixth ‘region 6’ 30 f havinga sixth particular type of similar behavior, and (7) a seventh ‘region7’ 30 g having a seventh particular type of similar behavior.

Referring to FIGS. 4 and 6, referring initially to FIG. 6, note that‘all available information’ associated with each of the grid blocks 22of the numerical simulation model 12 is used by the SOM software 14 togenerate and define ‘regions’ 30 of similar behavior among the gridblocks 22 of the numerical simulation model 12, and, responsive thereto,the SOM software 14 organizes the grid blocks 22 of the numericalsimulation model 12 into one or more of the defined ‘regions’ 30 a, 30b, 30 c, 30 d, 30 e, 30 f, and 30 g as illustrated in FIG. 4. In FIG. 6,for example, ‘all available information about grid block 1’ 32, and ‘allavailable information about grid block 2’ 34, . . . , and ‘all availableinformation about grid block N’ 36 is received by the SOM software 14.In response thereto, the SOM software 14 will ‘generate and defineregions of similar behavior based on all available informationassociated with the grid blocks’ as indicated by step 38 in FIG. 6. Whenthe ‘regions of similar behavior’ are defined, as indicated by step 40in FIG. 6, the SOM software 14 will organize the grid blocks 22 into oneor more ‘regions’ of similar behavior, as shown in FIG. 4. For example,as illustrated in FIG. 4, the SOM software 14 of FIGS. 1, 5, and 6 will:(1) organize the grid blocks 22 into a ‘region 1’ 30 a having a firsttype of similar behavior, (2) organize the grid blocks 22 into a ‘region2’ 30 b having a second type of similar behavior, (3) organize the gridblocks 22 into a ‘region 3’ 30 c having a third type of similarbehavior, (4) organize the grid blocks 22 into a ‘region 4’ 30 d havinga fourth type of similar behavior, (5) organize the grid blocks 22 intoa ‘region 5’ 30 e having a fifth type of similar behavior, (6) organizethe grid blocks 22 into a ‘region 6’ 30 f having a sixth type of similarbehavior, and (7) organize the grid blocks 22 into a ‘region 7’ 30 ghaving a seventh type of similar behavior.

Referring to FIG. 7, a block diagram 38 is illustrated which describeshow the SOM software 14 of FIG. 1, 5, and 6 will ‘Define Regions ofSimilar Behavior’, as indicated by step 38 in FIG. 6. The block diagram38 of FIG. 7 representing step 38 in FIG. 6 includes the followingsub-steps: step 38 a, step 38 b, step 38 c, and step 38 d. In order tofully understand step 38 of FIG. 6 which includes sub-steps 38 a-38 d asshown in FIG. 7, it would be helpful to read U.S. Pat. No. 6,950,786 toSonneland et al (hereinafter, the '786 Sonneland et al patent), issuedSep. 27, 2005, entitled “Method and Apparatus for Generating a CrossPlot in Attribute Space from a Plurality of Attribute Data Sets andGenerating a Class Data Set from the Cross Plot”, with particularreference to FIGS. 16 through 21 of the '786 Sonneland et al patent, thedisclosure of which is incorporated by reference into the specificationof this application. In FIG. 7, the SOM Software 14 will ‘define regionsof similar behavior’ (as indicated by step 38 in FIG. 6) by executingthe following steps: (1) Crossplot the parameters of the grid cells,step 38 a of FIG. 7, such as the parameters 24 of the grid cells 22 ofFIG. 2, (2) Identify clusters of points within the crossplot—the pointswithin a cluster represent grid cells having parameters which havesimilar behavior, step 38 b, (3) Plot the grid cells on amultidimensional plot while recalling the identity of those grid cellswithin the cluster which have similar behavior, step 38 c, and (4) grouptogether those grid cells on the multidimensional plot which clusteredtogether on the crossplot—that group is called a ‘region’, step 38 d.

A functional description of the operation of the present invention willbe set forth below with reference to FIGS. 1 through 7 of the drawings.

In FIG. 3, when ‘history matching’ the numerical simulation model 12,the ‘historically known input data’ is introduced as ‘input data’ to themodel 12 and, responsive thereto, the ‘output data’ 20 is generated.That ‘output data’ 20 is compared against a set of ‘historically knownoutput data’ which was previously generated (in the past) in response tothe ‘historically known input data’. When the ‘output data’ 20 does notsubstantially match the ‘historically known output data’, the numericalsimulation model 12 must first be ‘adjusted’ before the ‘historicallyknown input data’ 18 can again be introduced as ‘input data’ to themodel 12. In order to ‘adjust’ the model 12, refer to steps or block 26a and 26 b of FIG. 3. In step 26 a, in order to ‘adjust’ the model 12,certain ‘regions’ 30 of the model 12 of FIG. 4 must be defined andgenerated in the numerical simulation model 12. The ‘regions’ 30 of thenumerical simulation model 12 of FIG. 4 are defined and generated by theSOM software 14 of FIGS. 1, 5, and 6. The SOM software 14 will defineand generate the ‘regions’ 30 of FIG. 4 by executing the following stepsof FIG. 7 (refer to U.S. Pat. No. 6,950,786 to Sonneland et al, withparticular reference to FIGS. 16 through 21 of the '786 Sonneland et alpatent, the disclosure of which has already been incorporated herein byreference): (1) Crossplot the parameters of the grid cells, step 38 a ofFIG. 7, such as the parameters 24 of the grid cells 22 of FIG. 2, (2)Identify clusters of points within the crossplot—the points within acluster represent grid cells having parameters which have similarbehavior, step 38 b, (3) Plot the grid cells on a multidimensional plotwhile recalling the identity of those grid cells within the clusterwhich have similar behavior, step 38 c, and (4) group together thosegrid cells on the multidimensional plot which clustered together on thecrossplot—that group is called a ‘region’, step 38 d. When the ‘regions’are defined by the SOM software 14 in the numerical simulation model 12,in step 26 b of FIG. 3, it is necessary to multiply the ‘parameters’ 24in each grid block of each ‘region’ by a certain ‘value’. However, the‘value’ for one ‘region’ may be different from the ‘value’ for another‘region’ because ‘it is clear that the multiplier cannot be the samenumber for each grid block of the model’. At this point, the model 12has been ‘adjusted’. Then, the ‘historically known input data’ 18 isreintroduced, as ‘input data’, to the model 12, and, responsive thereto,the ‘output data’ 20 is generated once again. That ‘output data’ 20 iscompared against a set of ‘historically known output data’ which waspreviously generated (in the past) in response to the ‘historicallyknown input data’. When the ‘output data’ 20 does not substantiallymatch the ‘historically known output data’, the numerical simulationmodel 12 must be ‘re-adjusted’ in the same manner as discussed abovebefore the ‘historically known input data’ 18 can again be introduced as‘input data’ to the model 12. In step 26 b, in order to ‘re-adjust’ themodel 12, it may be necessary to: (1) use the SOM software 14 to definethe ‘regions’ 30 of the numerical simulation model 12 of FIG. 4 byexecuting the steps 38 a-38 d of FIG. 7 (a step which may have alreadybeen accomplished and therefore may not be necessary), and (2) multiplythe parameters 24 in each grid block of each newly defined ‘region’ by acertain value. Again, the ‘value’ for one ‘region’ may be different fromthe ‘value’ for another ‘region’ because ‘it is clear that themultiplier cannot be the same number for each grid block of the model’.At this point, the model 12 has been ‘re-adjusted’. Then, the‘historically known input data’ 18 is reintroduced, as ‘input data’, tothe model 12, and, responsive thereto, the ‘output data’ 20 is generatedonce again. This process repeats until the ‘output data’ 20 does, infact, substantially match the ‘historically known output data’. At thispoint, the numerical simulation model 12 has been ‘history matched’.

The above description, pertaining to the use of SOM's to define‘regions’ during the ‘history matching’ of numerical simulation models,being thus described, it will be obvious that the same may be varied inmany ways. Such variations are not to be regarded as a departure fromthe spirit and scope of the claimed method or apparatus or programstorage device, and all such modifications as would be obvious to oneskilled in the art are intended to be included within the scope of thefollowing claims.

1. A method of history matching a simulation model, comprising: (a)defining regions exhibiting similar behavior in said model therebygenerating said model having a plurality of regions, each of theplurality of regions exhibiting a similar behavior; (b) introducinghistorically known input data to said model; (c) generating output datafrom said model in response to said historically known input data; (d)comparing said output data from said model with a set of historicallyknown output data; (e) adjusting said model when said output data fromsaid model does not correspond to said set of historically known outputdata, the adjusting step including the step of arithmetically changingeach of the regions of said model; and (f) repeating steps (b), (c),(d), and (e) until said output data from said model does correspond tosaid set of historically known output data.
 2. The method of claim 1,wherein each region of said model includes a plurality of grid cells,each grid cell of each region having parameters associated therewith,and wherein the step of arithmetically changing each of the regions ofsaid model comprises: multiplying said parameters of each grid cell inone or more regions of said model by a value.
 3. The method of claim 2,wherein the step of defining regions exhibiting similar behavior in saidmodel comprises: crossploting the parameters of the grid cells on acrossplot, identifying clusters of points within the crossplot, thepoints within a cluster representing grid cells having parametersexhibiting similar behavior, plotting the grid cells on amultidimensional plot while recalling the identity of those grid cellswithin the cluster which have similar behavior, and grouping togetherthose grid cells on the multidimensional plot which clustered togetheron the crossplot, each group defining a region exhibiting similarbehavior.
 4. A program storage device readable by a machine tangiblyembodying a program of instructions executable by the machine to performmethod steps for history matching a simulation model, said method stepscomprising: (a) defining regions exhibiting similar behavior in saidmodel thereby generating said model having a plurality of regions, eachof the plurality of regions exhibiting a similar behavior; (b)introducing historically known input data to said model; (c) generatingoutput data from said model in response to said historically known inputdata; (d) comparing said output data from said model with a set ofhistorically known output data; (e) adjusting said model when saidoutput data from said model does not correspond to said set ofhistorically known output data, the adjusting step including the step ofarithmetically changing each of the regions of said model; and (f)repeating steps (b), (c), (d), and (e) until said output data from saidmodel does correspond to said set of historically known output data. 5.The program storage device of claim 4, wherein each region of said modelincludes a plurality of grid cells, each grid cell of each region havingparameters associated therewith, and wherein the step of arithmeticallychanging each of the regions of said model comprises: multiplying saidparameters of each grid cell in one or more regions of said model by avalue.
 6. The program storage device of claim 5, wherein the step ofdefining regions exhibiting similar behavior in said model comprises:crossploting the parameters of the grid cells on a crossplot,identifying clusters of points within the crossplot, the points within acluster representing grid cells having parameters exhibiting similarbehavior, plotting the grid cells on a multidimensional plot whilerecalling the identity of those grid cells within the cluster which havesimilar behavior, and grouping together those grid cells on themultidimensional plot which clustered together on the crossplot, eachgroup defining a region exhibiting similar behavior.
 7. A computerprogram adapted to be executed by a processor, said computer program,when executed by said processor, conducting a process for historymatching a simulation model, said process comprising: (a) definingregions exhibiting similar behavior in said model thereby generatingsaid model having a plurality of regions, each of the plurality ofregions exhibiting a similar behavior; (b) introducing historicallyknown input data to said model; (c) generating output data from saidmodel in response to said historically known input data; (d) comparingsaid output data from said model with a set of historically known outputdata; (e) adjusting said model when said output data from said modeldoes not correspond to said set of historically known output data, theadjusting step including the step of arithmetically changing each of theregions of said model; and (f) repeating steps (b), (c), (d), and (e)until said output data from said model does correspond to said set ofhistorically known output data.
 8. The computer program of claim 7,wherein each region of said model includes a plurality of grid cells,each grid cell of each region having parameters associated therewith,and wherein the step of arithmetically changing each of the regions ofsaid model comprises: multiplying said parameters of each grid cell inone or more regions of said model by a value.
 9. The computer program ofclaim 8, wherein the step of defining regions exhibiting similarbehavior in said model comprises: crossploting the parameters of thegrid cells on a crossplot, identifying clusters of points within thecrossplot, the points within a cluster representing grid cells havingparameters exhibiting similar behavior, plotting the grid cells on amultidimensional plot while recalling the identity of those grid cellswithin the cluster which have similar behavior, and grouping togetherthose grid cells on the multidimensional plot which clustered togetheron the crossplot, each group defining a region exhibiting similarbehavior.
 10. A system adapted for history matching a simulation model,comprising: first apparatus adapted for defining regions exhibitingsimilar behavior in said model thereby generating said model having aplurality of regions, each of the plurality of regions exhibiting asimilar behavior; second apparatus adapted for introducing historicallyknown input data to said model; third apparatus adapted for generatingoutput data from said model in response to said historically known inputdata; fourth apparatus adapted for comparing said output data from saidmodel with a set of historically known output data; fifth apparatusadapted for adjusting said model when said output data from said modeldoes not correspond to said set of historically known output data, thefifth apparatus including sixth apparatus adapted for arithmeticallychanging each of the regions of said model; and seventh apparatusadapted for repeating the functions performed by the second, third,fourth, fifth, and sixth apparatus until said output data from saidmodel does correspond to said set of historically known output data. 11.The system of claim 10, wherein each region of said model includes aplurality of grid cells, each grid cell of each region having parametersassociated therewith, and wherein the sixth apparatus adapted forarithmetically changing each of the regions of said model comprises:apparatus adapted for multiplying said parameters of each grid cell inone or more regions of said model by a value.
 12. The system of claim11, wherein the first apparatus adapted for defining regions exhibitingsimilar behavior in said model comprises: apparatus adapted forcrossploting the parameters of the grid cells on a crossplot, apparatusadapted for identifying clusters of points within the crossplot, thepoints within a cluster representing grid cells having parametersexhibiting similar behavior, apparatus adapted for plotting the gridcells on a multidimensional plot while recalling the identity of thosegrid cells within the cluster which have similar behavior, and apparatusadapted for grouping together those grid cells on the multidimensionalplot which clustered together on the crossplot, each group defining aregion exhibiting similar behavior.